


"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class    __a						( unittest.TestCase							):







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			1
					lowerCAmelCase_      							=			3
					lowerCAmelCase_      							=			(32, 32)

					lowerCAmelCase_      							=			floats_tensor((batch_size, num_channels) + sizes      ,	rng=random.Random(0   )   ).to(UpperCAmelCase   )
					return image







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					torch.manual_seed(0   )
					lowerCAmelCase_      							=			UNetaDConditionModel(
					    block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=4      ,	out_channels=4      ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,	cross_attention_dim=32      ,	)
					return model







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					torch.manual_seed(0   )
					lowerCAmelCase_      							=			AutoencoderKL(
					    block_out_channels=[32, 64]      ,	in_channels=3      ,	out_channels=3      ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,	latent_channels=4      ,	)
					return model







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					torch.manual_seed(0   )
					lowerCAmelCase_      							=			RobertaSeriesConfig(
					    hidden_size=32      ,	project_dim=32      ,	intermediate_size=37      ,	layer_norm_eps=1E-0_5      ,	num_attention_heads=4      ,	num_hidden_layers=5      ,	pad_token_id=1      ,	vocab_size=5006      ,	)
					return RobertaSeriesModelWithTransformation(UpperCAmelCase   )







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					def extract(*UpperCAmelCase      ,	**UpperCAmelCase   ):







						class    __a						:







										def __init__( self   ):

											'''simple docstring'''






											lowerCAmelCase_      							=			torch.ones([0]   )







										def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

											'''simple docstring'''






											self.pixel_values.to(UpperCAmelCase   )
											return self




						return Out()

					return extract







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					lowerCAmelCase_      							=			self.dummy_cond_unet
					lowerCAmelCase_      							=			PNDMScheduler(skip_prk_steps=UpperCAmelCase   )
					lowerCAmelCase_      							=			self.dummy_vae
					lowerCAmelCase_      							=			self.dummy_text_encoder
					lowerCAmelCase_      							=			XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''   )
					lowerCAmelCase_      							=			77

					lowerCAmelCase_      							=			self.dummy_image.to(UpperCAmelCase   )
					lowerCAmelCase_      							=			init_image / 2 + 0.5

					# make sure here that pndm scheduler skips prk
					lowerCAmelCase_      							=			AltDiffusionImgaImgPipeline(
					    unet=UpperCAmelCase      ,	scheduler=UpperCAmelCase      ,	vae=UpperCAmelCase      ,	text_encoder=UpperCAmelCase      ,	tokenizer=UpperCAmelCase      ,	safety_checker=UpperCAmelCase      ,	feature_extractor=self.dummy_extractor      ,	)
					lowerCAmelCase_      							=			VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor      ,	do_normalize=UpperCAmelCase   )
					lowerCAmelCase_      							=			alt_pipe.to(UpperCAmelCase   )
					alt_pipe.set_progress_bar_config(disable=UpperCAmelCase   )

					lowerCAmelCase_      							=			'''A painting of a squirrel eating a burger'''
					lowerCAmelCase_      							=			torch.Generator(device=UpperCAmelCase   ).manual_seed(0   )
					lowerCAmelCase_      							=			alt_pipe(
					    [prompt]      ,	generator=UpperCAmelCase      ,	guidance_scale=6.0      ,	num_inference_steps=2      ,	output_type='''np'''      ,	image=UpperCAmelCase      ,	)

					lowerCAmelCase_      							=			output.images

					lowerCAmelCase_      							=			torch.Generator(device=UpperCAmelCase   ).manual_seed(0   )
					lowerCAmelCase_      							=			alt_pipe(
					    [prompt]      ,	generator=UpperCAmelCase      ,	guidance_scale=6.0      ,	num_inference_steps=2      ,	output_type='''np'''      ,	image=UpperCAmelCase      ,	return_dict=UpperCAmelCase      ,	)[0]

					lowerCAmelCase_      							=			image[0, -3:, -3:, -1]
					lowerCAmelCase_      							=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase_      							=			np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5E-3
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 5E-3







				@unittest.skipIf(torch_device != '''cuda'''      ,	'''This test requires a GPU'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.dummy_cond_unet
					lowerCAmelCase_      							=			PNDMScheduler(skip_prk_steps=UpperCAmelCase   )
					lowerCAmelCase_      							=			self.dummy_vae
					lowerCAmelCase_      							=			self.dummy_text_encoder
					lowerCAmelCase_      							=			XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''   )
					lowerCAmelCase_      							=			77

					lowerCAmelCase_      							=			self.dummy_image.to(UpperCAmelCase   )

					# put models in fp16
					lowerCAmelCase_      							=			unet.half()
					lowerCAmelCase_      							=			vae.half()
					lowerCAmelCase_      							=			bert.half()

					# make sure here that pndm scheduler skips prk
					lowerCAmelCase_      							=			AltDiffusionImgaImgPipeline(
					    unet=UpperCAmelCase      ,	scheduler=UpperCAmelCase      ,	vae=UpperCAmelCase      ,	text_encoder=UpperCAmelCase      ,	tokenizer=UpperCAmelCase      ,	safety_checker=UpperCAmelCase      ,	feature_extractor=self.dummy_extractor      ,	)
					lowerCAmelCase_      							=			VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor      ,	do_normalize=UpperCAmelCase   )
					lowerCAmelCase_      							=			alt_pipe.to(UpperCAmelCase   )
					alt_pipe.set_progress_bar_config(disable=UpperCAmelCase   )

					lowerCAmelCase_      							=			'''A painting of a squirrel eating a burger'''
					lowerCAmelCase_      							=			torch.manual_seed(0   )
					lowerCAmelCase_      							=			alt_pipe(
					    [prompt]      ,	generator=UpperCAmelCase      ,	num_inference_steps=2      ,	output_type='''np'''      ,	image=UpperCAmelCase      ,	).images

					assert image.shape == (1, 32, 32, 3)







				@unittest.skipIf(torch_device != '''cuda'''      ,	'''This test requires a GPU'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/img2img/sketch-mountains-input.jpg'''   )
					# resize to resolution that is divisible by 8 but not 16 or 32
					lowerCAmelCase_      							=			init_image.resize((760, 504)   )

					lowerCAmelCase_      							=			'''BAAI/AltDiffusion'''
					lowerCAmelCase_      							=			AltDiffusionImgaImgPipeline.from_pretrained(
					    UpperCAmelCase      ,	safety_checker=UpperCAmelCase      ,	)
					pipe.to(UpperCAmelCase   )
					pipe.set_progress_bar_config(disable=UpperCAmelCase   )
					pipe.enable_attention_slicing()

					lowerCAmelCase_      							=			'''A fantasy landscape, trending on artstation'''

					lowerCAmelCase_      							=			torch.manual_seed(0   )
					lowerCAmelCase_      							=			pipe(
					    prompt=UpperCAmelCase      ,	image=UpperCAmelCase      ,	strength=0.7_5      ,	guidance_scale=7.5      ,	generator=UpperCAmelCase      ,	output_type='''np'''      ,	)
					lowerCAmelCase_      							=			output.images[0]

					lowerCAmelCase_      							=			image[255:258, 383:386, -1]

					assert image.shape == (504, 760, 3)
					lowerCAmelCase_      							=			np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2







@slow
@require_torch_gpu
class    __a						( unittest.TestCase							):







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/img2img/sketch-mountains-input.jpg'''   )
					lowerCAmelCase_      							=			init_image.resize((768, 512)   )
					lowerCAmelCase_      							=			load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''   )

					lowerCAmelCase_      							=			'''BAAI/AltDiffusion'''
					lowerCAmelCase_      							=			AltDiffusionImgaImgPipeline.from_pretrained(
					    UpperCAmelCase      ,	safety_checker=UpperCAmelCase      ,	)
					pipe.to(UpperCAmelCase   )
					pipe.set_progress_bar_config(disable=UpperCAmelCase   )
					pipe.enable_attention_slicing()

					lowerCAmelCase_      							=			'''A fantasy landscape, trending on artstation'''

					lowerCAmelCase_      							=			torch.manual_seed(0   )
					lowerCAmelCase_      							=			pipe(
					    prompt=UpperCAmelCase      ,	image=UpperCAmelCase      ,	strength=0.7_5      ,	guidance_scale=7.5      ,	generator=UpperCAmelCase      ,	output_type='''np'''      ,	)
					lowerCAmelCase_      							=			output.images[0]

					assert image.shape == (512, 768, 3)
					# img2img is flaky across GPUs even in fp32, so using MAE here
					assert np.abs(expected_image - image   ).max() < 1E-2



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_							=							{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							['MobileBertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							[
		    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MobileBertForMaskedLM',
		    'MobileBertForMultipleChoice',
		    'MobileBertForNextSentencePrediction',
		    'MobileBertForPreTraining',
		    'MobileBertForQuestionAnswering',
		    'MobileBertForSequenceClassification',
		    'MobileBertForTokenClassification',
		    'MobileBertLayer',
		    'MobileBertModel',
		    'MobileBertPreTrainedModel',
		    'load_tf_weights_in_mobilebert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							[
		    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMobileBertForMaskedLM',
		    'TFMobileBertForMultipleChoice',
		    'TFMobileBertForNextSentencePrediction',
		    'TFMobileBertForPreTraining',
		    'TFMobileBertForQuestionAnswering',
		    'TFMobileBertForSequenceClassification',
		    'TFMobileBertForTokenClassification',
		    'TFMobileBertMainLayer',
		    'TFMobileBertModel',
		    'TFMobileBertPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mobilebert import (
		    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileBertConfig,
		    MobileBertOnnxConfig,
		)
		from .tokenization_mobilebert import MobileBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mobilebert_fast import MobileBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilebert import (
				    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileBertForMaskedLM,
				    MobileBertForMultipleChoice,
				    MobileBertForNextSentencePrediction,
				    MobileBertForPreTraining,
				    MobileBertForQuestionAnswering,
				    MobileBertForSequenceClassification,
				    MobileBertForTokenClassification,
				    MobileBertLayer,
				    MobileBertModel,
				    MobileBertPreTrainedModel,
				    load_tf_weights_in_mobilebert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilebert import (
				    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileBertForMaskedLM,
				    TFMobileBertForMultipleChoice,
				    TFMobileBertForNextSentencePrediction,
				    TFMobileBertForPreTraining,
				    TFMobileBertForQuestionAnswering,
				    TFMobileBertForSequenceClassification,
				    TFMobileBertForTokenClassification,
				    TFMobileBertMainLayer,
				    TFMobileBertModel,
				    TFMobileBertPreTrainedModel,
				)

else:
		import sys

		lowercase_							=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)